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At least 937 records · Page 52

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Distributed deep learning training using silicon photonic switched architectures

The scaling trends of deep learning models and distributed training workloads are challenging network capacities in today’s datacenters and high-performance computing (HPC) systems. We propose a system architecture that leverages silicon photonic (SiP) switch-enabled server regrouping using bandwidth steering to tackle the challenges and accelerate distributed deep learning training. In addition, our proposed system architecture utilizes a highly integrated operating system-based SiP switch control scheme to reduce implementation complexity. To demonstrate the feasibility of our proposal, we built an experimental testbed with a SiP switch-enabled reconfigurable fat tree topology and evaluated the network performance of distributed ring all-reduce and parameter server workloads. The experimental results show up to 3.6× improvements over the static non-reconfigurable fat tree. Our large-scale simulation results show that server regrouping can deliver up to 2.3× flow throughput improvement for a 2× tapered fat tree and a further 11% improvement when higher-layer bandwidth steering is employed. The collective results show the potential of integrating SiP switches into datacenters and HPC systems to accelerate distributed deep learning training.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Technology Case Study: Economic, Sustainability, and Deployment Considerations for Sustainable Aviation Fuels Produced via Lignocellulosic Sugar Catalysis

This report presents a technology case study reflecting one exemplary representative pathway for the conversion of lignocellulosic sugars to sustainable aviation fuels (SAF) via aqueous phase reforming (APR) catalysis, considered within a broader integrated biorefinery framework based on biochemical processing operations. While far from the only option for converting sugars to SAF, this pathway was selected as a case study here based on its relatively high technology maturity and simplistic processing approach (avoiding complex separations or other equipment scalability challenges), coupled with the potential for high fuel yields and favorable costs/carbon intensities with opportunities for further near-term optimization. The report considers key process integration and engineering design considerations for a modeled hypothetical, nth-plant commercial biorefinery, reflecting a number of processing options and parameters envisioned to be achievable as future goals. Resultant outputs from Aspen Plus process simulations are evaluated through techno-economic and life cycle analyses (TEA and LCA), including implications for marginal cost of CO2 abatement and inclusion of currently-applicable policy incentives. Moving beyond base case configurations, a number of alternative scenarios are also evaluated for their ability to further improve economics, greenhouse gas (GHG) emissions, and marginal cost of abatement, highlighting a path to achieve deep decarbonization goals of more than 70% GHG reduction for SAF (with the potential to reach net-negative carbon intensities in some cases) under reasonable fuel production costs. The report also highlights future opportunities and gaps for further research on this technology pathway.

09 BIOMASS FUELS↗

NASA NTRS1979

Control considerations for CCV fighters at high angles of attack

Wind tunnel and piloted simulation studies were conducted to investigate the potential high angle of attack control problems that are introduced by the use of the CCV concept of relaxed static pitch stability (RSS) on fighter aircraft. A conventional wing/aft tail design incorporating modest levels of static instability and a close-coupled canard/wing design exhibiting very high levels of instability was investigated. Two types of high angle of attack control problems can result from the use of RSS: pitch departures caused by coupling and deep stall trim. Avoidance of these problems requires that the airplane have sufficient nose-down pitch control at high angles of attack. The effectiveness of several pitch control configurations were investigated including conventional aft-mounted stabilators, wing-mounted elevators, canard-mounted flaps, and all-moveable canards. Varying the incidence of the canards was the most effective scheme; however, very large deflections may be required on highly unstable configurations to prevent pitch departure without sacrificing roll performance and to avoid deep stall trim. For situations where the high angle of attack pitch control requirement is not met, control laws were developed to inhibit the departure and to allow deep stall recovery. However, these schemes involve limiting airplane roll capability and therefore can potentially compromise maneuverability.

Nguyen, L. T.↗

NASA NTRS2016

Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of Smart Deep Brain Stimulation

A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable "smart" therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

bioinstrumentation↗

NASA NTRS2016

Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of "Smart" Deep Brain Stimulation

A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable smart therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

human body↗

DOE OSTI2016

Large dielectric constant, high acceptor density, and deep electron traps in perovskite solar cell material CsGeI 3

Here we report that many metal halides that contain cations with the ns 2 electronic configuration have recently been discovered as high-performance optoelectronic materials. In particular, solar cells based on lead halide perovskites have shown great promise as evidenced by the rapid increase of the power conversion efficiency. In this paper, we show density functional theory calculations of electronic structure and dielectric and defect properties of CsGeI 3 (a lead-free halide perovskite material). The potential of CsGeI 3 as a solar cell material is assessed based on its intrinsic properties. We find anomalously large Born effective charges and a large static dielectric constant dominated by lattice polarization, which should reduce carrier scattering, trapping, and recombination by screening charged defects and impurities. Defect calculations show that CsGeI 3 is a p-type semiconductor and its hole density can be modified by varying the chemical potentials of the constituent elements. Despite the reduction of long-range Coulomb attraction by strong screening, the iodine vacancy in CsGeI3 is found to be a deep electron trap due to the short-range potential, i.e., strong Ge–Ge covalent bonding, which should limit electron transport efficiency in p-type CsGeI 3 . This is in contrast to the shallow iodine vacancies found in several Pb and Sn halide perovskites (e.g., CH 3 NH 3 PbI 3 , CH 3 NH 3 SnI 3 , and CsSnI 3 ). The low-hole-density CsGeI 3 may be a useful solar absorber material but the presence of the low-energy deep iodine vacancy may significantly reduce the open circuit voltage of the solar cell. Still, on the other hand, CsGeI 3 may be used as an efficient hole transport material in solar cells due to its small hole effective mass, the absence of low-energy deep hole traps, and the favorable band offset with solar absorber materials such as dye molecules and CH 3 NH 3 PbI 3 .

36 MATERIALS SCIENCE↗

NASA NTRS2012

The Potential for a Ka-band (32 GHz) Worldwide VLBI Network

Ka-band (32 GHz, 9mm) Very Long Baseline Interferometric (VLBI) networking has now begun and has tremendous potential for expansion over the next few years. Ka-band VLBI astrometry from NASA's Deep Space Network has already developed a catalog of 470 observable sources with highly accurate positions. Now, several antennas worldwide are planning or are considering adding Ka-band VLBI capability. Thus, there is now an opportunity to create a worldwide Ka-band network with potential for high resolution imaging and astrometry. With baselines approaching a Giga-lambda, a Ka-band network would be able to probe source structure at the nano-radian (200 as) level ( 100X better than Hubble) and thus gain insight into the astrophysics of the most compact regions of emission in active galactic nuclei. We discuss the advantages of Ka-band, show the known sources and candidates, simulate projected baseline (uv) coverage, and discuss potential radio frequency feeds. The combination of these elements demonstrates the feasibility of a worldwide Ka network within the next few years!

quasar↗

NASA NTRSDate not supplied

Supporting Crew Medical Decisions on Deep Space Missions: A Real-Time Performance Monitoring

Crewed missions into deep space will require astronauts to respond autonomously to safety- and time-critical anomalies. These and other potential issues will require the continuous monitoring and recognition of potentially subtle, but complex, anomalous data patterns. NASA continues to investigate real-time metrics for Extravehicular Operations1 and for providing feedback to improve performance2. Since it is unknown how long-term exposure to deep space will affect crew health, continuous monitoring of clinical and subclinical status is warranted. NASA is characterizing crew medical decision-support needs and identifying the recommended requirements of a Clinical Decision Support System (CDSS) within the Exploration Medical Capability Element (ExMC) of the Human Research Program3. Early detection, and continuous monitoring, of performance may provide an effective prognostic capability for assessing crew health state, and the identified requirements could be provided for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. There is evidence to suggest that automatic, unobtrusive collection of keystroke features, such as alphanumeric key latencies, backspaces or typing rhythm, could be a valuable performance screening tool. Keystroke features show significant differences between control subjects and patients with overt clinical illness, such as multiple sclerosis4 and Parkinson’s disease5, as well as behavioral health and performance issues, such as mild cognitive impairment6 and depression7. This work is exploring a capability for the assessment of early task performance decline in crew as they perform daily activities. Timestamped keyboard entries are continuously collected and stored. Nominal keystroke interactions are compared to those obtained after exposure to spaceflight stressors, e.g., fatigue, to determine the methods sensitivity. This work will pave the way toward an objective detection tool that may be deployed in a spaceflight setting.

clinical↗

DOE OSTI2023

Deep Reinforcement Learning Based Smart Water Heater Control for Reducing Electricity Consumption and Carbon Emission

Water heating is the third largest electricity consumer in U.S. households, after space heating and cooling. Thus, water heaters represent a significant potential for reducing electricity consumption and associated CO2 emissions of residential buildings. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity consumption and the CO2 emissions of a heat pump water heater without affecting user comfort. In this approach, a set of RL agents focusing on either electricity saving or emission reduction, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on different hot water usage and Marginal Operating Emissions Rate (MOER) profiles. The testing results showed that the RL agents that focus on electricity saving can save electricity in the range of 12–22% by operating the water heater with maximum heat pump efficiency and minimum electric element utilization. On the other hand, the RL agents that focus on emission reduction reduced emissions in the range of 18–37% by making use of the variable MOER values. These RL agents used the heat pump and/or an element when the MOER values are low due to the availability of renewable energy sources (e.g., solar and wind) and mostly avoided the periods of carbon-intensive periods. Overall, these results showed that the proposed RL approach can help minimize the electricity consumption and the CO2 emissions of a heat pump water heater without having any prior knowledge about the device.

Amasyali, Kadir↗

DOE OSTI2024

Research Priorities and Opportunities in U.S. Wholesale Electricity Markets: Market Design under Deep Decarbonization

This report contributes to the body of literature by reviewing key challenges for competitive wholesale electricity market design in deeply decarbonized power systems and establishing potential solutions and associated research needs. We provide a long-term perspective on ways in which competitive wholesale electricity markets can evolve to ensure that they still operate efficiently throughout the transition to a deeply decarbonized future. In this report, we use the term “deep decarbonization” to refer broadly to systems that generate nearly all their electricity from zero-carbon or carbon-neutral resources, while recognizing that there are many potential configurations of such systems. We also stress that many of these issues will arise to varying degrees as systems move toward full decarbonization, even if they are not truly 100% carbon-free.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2022

TruePAL – An AI Assistant for First Responder Safety

This paper presents the development of an AI assistant, Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL), to provide real-time warning of risks of potential crashes to the first responders. The TruePAL system employs an AI and deep learning technology for saving first responders and roadside crews lives in and around active traffic. A deep neural network (DNN) and a Non-Axiomatic Reasoning System (NARS) are implemented as an AI system. A mobile app with AI interface is developed to perform verbal communication with the first responders. The TruePAL team has developed an explainable AI approach by opening up the DNN blackbox to extract the activation filters of various features and parts of the targeted objects. The combination of DNN and NARS makes the TruePAL system explainable to the users. TruePAL ingests on-board cameras, radar, and other sensor signals, analyzes the environment and traffic patterns to generate timely warning to drivers and roadside crews to avoid crashes. The TruePAL team, in collaboration with the Miami/Dade Police Dept., has designed five use cases and multiple sub-scenarios in a CARLA driving simulator to test the capability of TruePAL in timely warning to the first responder drivers in potential crash scenarios. We have successfully demonstrated its capability of timely warning in over a dozen scenarios based on the use cases. The preliminary test simulation results show that TruePAL could provide the drivers and crew members advanced warning before a crash occurs.

Chow, Edward↗

DOE OSTI2024

Trap‐Engineering the Persistent Luminescence of Ca 3 Ga 4 O 9 :Tb 3+ via Al 3+ Substitution for Optical Data Storage

Abstract Optically stimulated luminescence (OSL) materials hold great potential for optical data storage (ODS) and anticounterfeiting applications. Nevertheless, the scarcity of suitable luminescent materials with deep‐level traps remains a significant obstacle. Herein, a host substation strategy have been employed to tune the persistent luminescence (PersL) and OSL properties of Ca 3 Ga 4 O 9 :Tb 3+ by Al 3+ substitution through trap engineering and demonstrated their potential. Specifically, the photoluminescence of the Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ of Tb 3+ is first investigated due to its different occupancies of Ca 2+ . The influence of host substitution on the crystal structure, trap depth, trap density, PersL, and OSL properties have further investigated. A series of strong PersL and OSL peaks from the Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ with bluish‐green emissions have been observed. The Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ have shown controllable photon release upon thermal and optical stimuli, enhancing their performance for ODS. Thermally stimulated luminescence suggests that vacancy and defect concentrations inside the Ca 3‐x% (Ga 1‐y% Al y% ) 4 O 9 :x%Tb 3+ can be manipulated by Tb 3+ doping and Al 3+ substitution, which ultimately leads to the formation of deep traps and a broad distribution of traps with increased deep trap concentration. The work demonstrates that trap engineering through Al 3 ⁺ substitution is an effective method for tuning PersL and OSL properties of Ca 2.985 (Ga 1‐y% Al y% ) 4 O 9 :0.5%Tb 3+ for ODS.

Abeywickrama, Thulitha M. [Department of Chemistry↗

DOE OSTI2015

Photoinduced reduction of surface states in Fe:ZnO

We report on the electronic structure of nano-crystalline Fe:ZnO, which has recently been found to be an efficient photocatalyst. Using resonant photoemission spectroscopy, we determine the binding energy of Fe 3d states corresponding to different valencies and coordination of the Fe atoms. The photo-activity of ZnO reduces Fe from 3+ to 2+ in the surface region of the nano-crystalline material due to the formation of oxygen vacancies. Electronic states corresponding to low-spin Fe{sup 2+} are observed and attributed to crystal field modification at the surface. These states are potentially important for the photocatalytic sensitivity to visible light due to their location deep in the ZnO bandgap. X-ray absorption and x-ray photoemission spectroscopy suggest that Fe is only homogeneously distributed for concentrations up to 3%. Increased concentrations does not result in a higher concentration of Fe ions in the surface region. This is limiting the photocatalytic functionality of ZnO, where the most efficient Fe doping concentration has been shown to be 1%-4%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Offshore Wind Resource, Cost, and Economic Potential in the State of Maine

This report provides information for decision-makers about floating offshore wind technologies in the state of Maine. It summarizes research efforts performed at the National Renewable Energy Laboratory between 2015 and 2017 to analyze the resource potential, cost of offshore wind, and economic potential of offshore wind from four primary reports: Musial et al. (2016); Beiter et al. (2016, 2017); and Mone et al. (unpublished). From Musial et al. (2016), Maine's technical offshore wind resource potential ranked seventh in the nation overall with more than 411 terawatt-hours/year of offshore resource generating potential. Although 90% of this wind resource is greater than 9.0-meters-per-second average velocity, most of the resource is over deep water, where floating wind technology is needed. Levelized cost of energy and levelized avoided cost of energy were computed to estimate the unsubsidized 'economic potential' for Maine in the year 2027 (Beiter et al. 2016, 2017). The studies found that Maine may have 65 gigawatts of economic potential by 2027, the highest of any U.S. state. Bottom-line costs for the Aqua Ventus project, which is part of the U.S. Department of Energy's Advanced Technology Demonstration project, were released from a proprietary report written by NREL in 2016 for the University of Maine (Mone et al. unpublished). The report findings were that economies of scale and new technology advancements lowered the cost from $300/megawatt-hour (MWh) for the two-turbine 12-megawatt (MW) Aqua Ventus 1 project, to $126/MWh for the commercial-scale, 498-MW Aqua Ventus-2 project. Further cost reductions to $77/MWh were found when new technology advancements were applied for the 1,000-MW Aqua Ventus-3 project in 2030. No new analysis was conducted for this report.

17 WIND ENERGY↗

DOE OSTI2021

Deep learning inversion of gravity data for detection of CO 2 plumes in overlying aquifers

In this work, we developed an effective U-Net based deep learning (DL) model for inversion of surface gravity data on a rectangular grid to predict 2-D high-resolution subsurface CO 2 distribution along a vertical cross-section due to CO 2 leakage through a wellbore within a deep CO 2 storage reservoir. We used synthetic data to model two types of CO 2 leakage scenarios: one CO 2 plume in a shallow aquifer (single plume case), and two plumes present at different depths (double plume case). The 3-D synthetic plume samples were created by sampling among predetermined CO 2 plume depths, saturations, and volumes. The corresponding surface gravity data on a rectangular grid were generated by a 3-D forward model. The U-Net model detected 72% of single-plume samples, and one or both plumes in 75% of double-plume samples. Most of the undetected single plumes have small gravity field strengths below the typical noise level of 5 μGal. This model generated reproducible, reliable predictions with acceptable errors and demonstrated improved spatial resolution over the conventional least-squares inversion. In contrast to the conventional least-squares inversion, which often overestimates the size of its target and underestimates its density, this U-Net model accurately delineated the boundary of a target. Furthermore, this DL inversion detected deep, small, or low saturation CO 2 plumes that are often more difficult to resolve with conventional gravity inversion methods. We note the limitations of this feasibility study, including the use of synthetic data with regular CO 2 plume shapes, and the prediction of a 2-D plume cross-section rather than the full 3-D plume, as well, we recognize the lower detection fraction for double-plume scenarios. Nevertheless, this study demonstrates that DL gravity inversion is a promising and potentially superior method to conventional least-squares inversion. Our U-Net based deep learning inversion approach may be adapted for inversion of other types of geophysical data. DL inversion can facilitate near real-time monitoring of geologic carbon sequestration to provide site operators with prompt information about subsurface CO 2 distribution for risk management and mitigation.

58 GEOSCIENCES↗

DOE OSTI2024

Low‐Temperature Synthesis of Stable CaZn 2 P 2 Zintl Phosphide Thin Films as Candidate Top Absorbers

Abstract The development of tandem photovoltaics and photoelectrochemical solar cells requires new absorber materials with bandgaps in the range of ≈1.5–2.3 eV, for use in the top cell paired with a narrower‐gap bottom cell. An outstanding challenge is finding materials with suitable optoelectronic and defect properties, good operational stability, and synthesis conditions that preserve underlying device layers. This study demonstrates the Zintl phosphide compound CaZn 2 P 2 as a compelling candidate semiconductor for these applications. Phase‐pure, ≈500 nm‐thick CaZn 2 P 2 thin films are prepared using a scalable reactive sputter deposition process at growth temperatures as low as 100 °C, which is desirable for device integration. Ultraviolet‐visible spectroscopy shows that CaZn 2 P 2 films exhibit an optical absorptivity of ≈10 4 cm −1 at ≈1.95 eV direct bandgap. Room‐temperature photoluminescence (PL) measurements show near‐band‐edge optical emission, and time‐resolved microwave conductivity (TRMC) measurements indicate a photoexcited carrier lifetime of ≈30 ns. CaZn 2 P 2 is highly stable in both ambient conditions and moisture, as evidenced by PL and TRMC measurements. Experimental data are supported by first‐principles calculations, which indicate the absence of low‐formation‐energy, deep intrinsic defects. Overall, this study shall motivate future work integrating this potential top cell absorber material into tandem solar cells.

14 SOLAR ENERGY↗

DOE OSTI2020

Dynamic changes in dissolved organic matter composition in a Mountain Lake under ice cover and relationships to changes in nutrient cycling and phytoplankton community composition

We studied Bear Lake, a sub-alpine oligotrophic lake in Rocky Mountain National Park, Colorado U.S.A, during the winter ice cover period of 2018. Our goal was to understand relationships between biogeochemical processes and the chemical quality of the dissolved organic matter, a major pool of organic carbon in all lakes. The lake exhibited a highly stratified environment with limited light availability, a consistent oxycline at 7 meters, and dilute hydrochemistry. Nutrient concentrations were low and depletion of nitrogen, particularly for NH4 to a lesser extent NO3, were related to increasing phytoplankton abundance. The deep chlorophyll maxima that developed above the oxycline was dominated by a potentially mixotrophic dinoflagellate. DOM chemical quality as measured through high-resolution mass spectrometry was related to these shifts in aquatic ecosystem structure. However, CDOM spectroscopy of the whole water was a poor indicator of biogeochemical changes in the DOM pool. The spectrometry data highlighted an evolving and contrasting reservoir of phosphorus, nitrogen, and sulfur-containing organic compounds sensitive to nutrient cycling and redox conditions. We also observed reduced chemodiversity between the surface and bottom water DOM molecular formulas over time. Homogeneity increased in the surface and more unique compounds were produced in the bottom waters, where an overall increase occurred in the favorability of carbon to support microbial respiration. Our results show dynamic chemical changes in the DOM pool reflect dynamic nutrient cycling under ice cover, potentially driven by mixotrophic growth of the dinoflagellate Gymnodinium sp.

Rue, Garrett↗

DOE OSTI2013

Experimental study of potential wellbore cement carbonation by various phases of carbon dioxide during geologic carbon sequestration

Hydrated Portland cement was reacted with carbon dioxide (CO2) in supercritical, gaseous, and aqueous phases to understand the potential cement alteration processes along the length of a wellbore, extending from deep CO2 storage reservoir to the shallow subsurface during geologic carbon sequestration. The 3-D X-ray microtomography (XMT) images displayed that the cement alteration was significantly more extensive by CO2-saturated synthetic groundwater than dry or wet supercritical CO2 at high P (10 MPa)-T (50°C) conditions. Scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) analysis also exhibited a systematic Ca depletion and C enrichment in cement matrix exposed to CO2-saturated groundwater. Integrated XMT, XRD, and SEM-EDS analyses identified the formation of extensive carbonated zone filled with CaCO3(s), as well as the porous degradation front and the outermost silica-rich zone in cement after exposure to CO2-saturated groundwater. The cement alteration by CO2-saturated groundwater for 2-8 months overall decreased the porosity from 31% to 22% and the permeability by an order of magnitude. Cement alteration by dry or wet supercritical CO2 was slow and minor compared to CO2-saturated groundwater. A thin single carbonation zone was formed in cement after exposure to wet supercritical CO2 for 8 months or dry supercritical CO2 for 15 months. Extensive calcite coating was formed on the outside surface of a cement sample after exposure to wet gaseous CO2 for 1-3 months. The chemical-physical characterization of hydrated Portland cement after exposure to various phases of carbon dioxide indicates that the extent of cement carbonation can be significantly heterogeneous depending on CO2 phase present in the wellbore environment. Both experimental and geochemical modeling results suggest that wellbore cement exposure to supercritical, gaseous, and aqueous phases of CO2 during geologic carbon sequestration is unlikely to damage the wellbore integrity because cement alteration by all phases of CO2 is dominated by carbonation reaction. This is consistent with previous field studies of wellbore cement with extensive carbonation after exposure to CO2 for 3 decades. However, XMT imaging indicates that preferential cement alteration by supercritical CO2 or CO2-saturated groundwater can occur along the cement-steel or cement-rock interfaces. This highlights the importance of further investigation of cement degradation along the interfaces of wellbore materials to ensure permanent geologic carbon storage.

Calcite↗